Progress report 3
Sequence Processing and classification
Updated: 2019-05-15 11:50:31
Descriptive statistics of assembled sequence
| SequenceLength | OverlapLength | MisMatches | |
|---|---|---|---|
| Min. : 14 | Min. : 0.0 | Min. : 0.00 | |
| 1st Qu.:252 | 1st Qu.:102.0 | 1st Qu.: 0.00 | |
| Median :252 | Median :153.0 | Median : 0.00 | |
| Mean :241 | Mean :137.7 | Mean : 1.38 | |
| 3rd Qu.:253 | 3rd Qu.:173.0 | 3rd Qu.: 0.00 | |
| Max. :500 | Max. :250.0 | Max. :116.00 |
Features of assembled sequences
Descriptive statistics of aligned sequence
| QueryLength | AlignmentLength | PercentIdentity | |
|---|---|---|---|
| Min. :100.0 | Min. :-1595.0 | Min. : 50.00 | |
| 1st Qu.:252.0 | 1st Qu.: 252.0 | 1st Qu.: 89.68 | |
| Median :253.0 | Median : 253.0 | Median : 92.09 | |
| Mean :246.4 | Mean : 246.5 | Mean : 91.30 | |
| 3rd Qu.:253.0 | 3rd Qu.: 253.0 | 3rd Qu.: 94.86 | |
| Max. :300.0 | Max. : 303.0 | Max. :100.00 |
Features of aligned sequences
Publication-ready figure
Subplots of assembled (blue) and aligned (green) sequences are grouped together
View the first 10 rows of prosecced data
| SampleID | Original | Screened | Aligned | Denoised | NonChimeric | BacteriaOnly | NoMock |
|---|---|---|---|---|---|---|---|
| F3D000 | 7786 | 6836 | 6813 | 6810 | 6374 | 6369 | 6369 |
| F3D001 | 5862 | 5026 | 5009 | 5009 | 4712 | 4705 | 4705 |
| F3D002 | 19610 | 17355 | 17277 | 15105 | 13918 | 13852 | 13852 |
| F3D003 | 6756 | 5955 | 5920 | 11068 | 10057 | 10055 | 10055 |
| F3D005 | 4444 | 3861 | 3844 | 14846 | 13869 | 13831 | 13831 |
| F3D006 | 7985 | 7048 | 7014 | 5224 | 4784 | 4784 | 4784 |
| F3D007 | 5124 | 4538 | 4510 | 2773 | 2521 | 2516 | 2516 |
| F3D008 | 5292 | 4639 | 4611 | 2763 | 2472 | 2472 | 2472 |
| F3D009 | 7065 | 6247 | 6208 | 4048 | 3596 | 3596 | 3596 |
| F3D011 | 17774 | 15226 | 15127 | 6353 | 5734 | 5732 | 5732 |
Summary statistics of processed sequences
Shows summary of the exact number of sequences remaining at each step in 4-quantiles.
Original Screened Aligned Denoised
Min. : 14 Min. : 8 Min. : 6 Min. : 6
1st Qu.: 5365 1st Qu.: 4688 1st Qu.: 4658 1st Qu.: 4656
Median : 7996 Median : 7046 Median : 7012 Median : 7008
Mean :10088 Mean : 8811 Mean : 8767 Mean : 8762
3rd Qu.:13630 3rd Qu.:11928 3rd Qu.:11870 3rd Qu.:11863
Max. :40077 Max. :34820 Max. :34589 Max. :34566
NonChimeric BacteriaOnly NoMock
Min. : 6 Min. : 6 Min. : 6
1st Qu.: 4331 1st Qu.: 4322 1st Qu.: 4322
Median : 6580 Median : 6568 Median : 6568
Mean : 8114 Mean : 8108 Mean : 8108
3rd Qu.:10737 3rd Qu.:10731 3rd Qu.:10731
Max. :32147 Max. :32142 Max. :32142
View the first 10 row of sequence length data
# A tibble: 10 x 7
Original Screened Aligned Denoised NonChimeric BacteriaOnly NoMock
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 NA NA NA NA NA NA NA
2 252 252 252 252 252 252 252
3 252 252 252 252 252 252 252
4 253 253 253 252 252 252 252
5 253 155 252 252 252 252 252
6 252 252 253 252 252 252 252
7 124 252 252 253 253 253 253
8 252 253 252 252 252 252 252
9 253 252 253 253 253 253 253
10 253 252 211 253 253 253 253
Graphical distribution of processed sequences
Stacked barplot
Note that it for large number of samples it is difficult to plot the x-axis. In such situation it is good to split the samples (see example below)
Subsampling examples
Below is a command for ssampling a specific dataset size. Here it shows how to filter samples with less that 2000 sequences.
Samples with less than 2000 sequences
library(dplyr)
subsetlt2000 <- seqcount.v.m %>% as.data.frame() %>% dplyr::filter(value <2000)
Barplots grouped by process
Shows maximum sequence depth.
Boxplots grouped by process
Boxplots grouped by sex
Boxplots grouped by time
Density plots grouped by sex and time
Histograms grouped by sex and time
Publication-ready figure
- All subplots on single plot:
Posible questions
At sequence processing
- QN1: Do the paired sequences overlap as expected?
- QN2: Are there sequences to be removed from the analysis based on sequence length?
- QN3: Does the dorminant length match the targeted region of 16S rRNA gene?
- QN4: Are the representative sequences too few or too many? Note that too many non-redundant sequences may results from poor overlapping between forward and reverse reads. Review before proceeding to avoid misleading conclusions.
At sequence classification
- QN1: What taxonomic classifier should be used? Think of need to develop and use custom classifier that suits the study objectives.
- QN2: Are there sequences assigned to non-bacterial taxonomic lineages?
- QN3: Should the matches to non-bacterial lineages be removed from further analysis?
- QN4: …….?
At error estimation
- QN1: Is the error too high to cause rejection of the results results?
- QN2: …….?
- QN3: …….?
- QN4: …….?
Summary of packages used in the analysis
R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] scales_1.0.0 ggpubr_0.2 magrittr_1.5 dplyr_0.8.0.1 ggplot2_3.1.0
[6] readr_1.3.1
loaded via a namespace (and not attached):
[1] Biobase_2.42.0 tidyr_0.8.3 jsonlite_1.6
[4] splines_3.5.2 foreach_1.4.4 assertthat_0.2.1
[7] highr_0.8 stats4_3.5.2 phyloseq_1.26.1
[10] yaml_2.2.0 slam_0.1-45 pillar_1.3.1
[13] lattice_0.20-38 glue_1.3.1 digest_0.6.18
[16] XVector_0.22.0 colorspace_1.4-1 cowplot_0.9.4
[19] htmltools_0.3.6 Matrix_1.2-15 plyr_1.8.4
[22] tm_0.7-6 pkgconfig_2.0.2 microbiome_1.4.2
[25] zlibbioc_1.28.0 purrr_0.3.2 tibble_2.1.1
[28] mgcv_1.8-27 IRanges_2.16.0 withr_2.1.2
[31] BiocGenerics_0.28.0 lazyeval_0.2.1 cli_1.1.0
[34] NLP_0.2-0 survival_2.43-3 crayon_1.3.4
[37] evaluate_0.13 fansi_0.4.0 nlme_3.1-137
[40] MASS_7.3-51.1 xml2_1.2.0 vegan_2.5-4
[43] tools_3.5.2 data.table_1.12.0 hms_0.4.2
[46] stringr_1.4.0 Rhdf5lib_1.4.3 S4Vectors_0.20.1
[49] munsell_0.5.0 cluster_2.0.7-1 Biostrings_2.50.2
[52] ade4_1.7-13 compiler_3.5.2 rlang_0.3.4
[55] rhdf5_2.26.2 grid_3.5.2 iterators_1.0.10
[58] biomformat_1.10.1 igraph_1.2.4 labeling_0.3
[61] rmarkdown_1.12 gtable_0.2.0 codetools_0.2-16
[64] multtest_2.38.0 reshape2_1.4.3 iNEXT_2.0.19
[67] R6_2.4.0 knitr_1.22 utf8_1.1.4
[70] permute_0.9-4 ape_5.2 stringi_1.4.3
[73] parallel_3.5.2 Rcpp_1.0.1 tidyselect_0.2.5
[76] xfun_0.6